Download Simulating urban vegetation dynamics and

Simulating the urban surface water balance based on
detailed hyperspectral and frequent multispectral
remote sensing data
CW, Wirion1*, WB, Bauwens1, BV, Verbeiren1,
1
Department of hydrology and hydraulic engineering,
Vrije Universiteit Brussel (VUB)
Brussels, Belgium
[email protected]
Abstract— This study proposes an integrated methodology to
simulate the urban water balance using remote sensing data. We
derive an urban land-cover map at a high spatial resolution (2m)
using a hyperspectral APEX image, and we characterize the
seasonal variation of the urban green with the near-daily ProbaV products (100m). We validate distributed LAI maps, derived
from Proba-V data, with a detailed mapping of vegetation cover
throughout the season. We collected, for this purpose, LAI
measurements with the SS1 Sunscan system of all trees within a
Proba-V pixel. The outcome is a time series of validated LAI
maps which enable a detailed characterization of urban
vegetation dynamics. The distributed land-cover map, as well as
LAI maps are used as input data for the process-based and
spatially-distributed WetSpa-Python model, to simulate the
urban water balance over one year for the Watermaelbeek
catchment in Brussels at a 2m resolution. The LAI maps allow
improving the parameterization of the interception storage of the
urban vegetation. Consequently, the dynamics in spatio-temporal
distribution of precipitation reaching and evapotranspiration
leaving the urban land surface are better simulated.
Keywords: APEX hyperspectral, Proba-V timeseries, high
resolution modelling, interception capacity
I. INTRODUCTION
The development of urban areas leads to a rapid
transformation of the land cover (e.g. vegetation, bare soil,
water and man-made surfaces) and of the land surface
properties (e.g. heat capacity, soil moisture, vegetation density
and diversity, surface roughness) and therefore highly affects
the energy and water cycles of these areas ([2];[4]). However,
simulating the hydrological response of urban landscapes
remains challenging due to the high heterogeneity of the
catchment. The characterization of heterogeneous urban areas
can be performed using multi-resolution remote sensing (RS)
data. RS provides the possibility to classify surfaces
objectively into land cover classes and to monitor seasonal
dynamics as well as the changes of land cover classes over a
period of years. However, parameterization of urban land
cover with RS techniques remains a complex task and it is still
not clear if and to what extent satellite data can be used for
heterogeneous urban areas.
In this study we use multi-source remote sensing data in order
to improve the parameterization of a hydrological model. The
high spatial resolution of the hyperspectral APEX (Airborne
Prism EXperiment) image (2 m) allows characterizing the
urban land cover in detail, whereas the high (near-daily)
temporal resolution of the multispectral Proba-V (Project for
OnBoard Autonomy – Vegetation) images allows to account
for seasonal variations of the urban green. The Water and
Energy Transfer between Soil Plants and Atmosphere tool
(WetSpa) ([6]; [3]) allows for a detailed simulation of the
hydrological processes at the surface in a continuous and
distributed manner.
II. MATERIALS AND METHODS
The generation of the urban land-cover map is based on the
airborne hyperspectral APEX image with a 2m resolution.
Based on 200 known pixels, so-called training pixels, per class
a supervised classifier, the Support Vector Machine (SVM)
classifier, was applied to the APEX image to generate the
high-resolution land-cover map of the Watermaelbeek
catchment using 31 sub-classes such as impervious roofing
and pavements (tiles, asphalt, concrete) or pervious vegetated
(trees, shrubs, grasses) and non-vegetated surfaces (water,
bare soil) [5].
To create LAI maps based on the RS data, we first calculate
the normalized difference vegetation index (NDVI) [8] and
then the LAI using the method of Su [7]. The method for
generating a time series of LAI maps at a 2m resolution is
inspired by [1], who developed a disaggregation method
(DisNDVI) for generating a time series of NDVI images
aiming at a spatial resolution of 60 m. For our study we want
to create LAI maps with a spatial resolution of 2 m based on
the APEX and Proba-V data.
The ground-truthing of RS data includes a detailed mapping of
land-cover characteristics and more specifically vegetation
cover throughout the seasons. A Proba-V pixel on the VUB
campus in Brussels, Belgium is selected to monitor the
dynamics of the tree canopies from April to October 2015.
Within the 100 x 100m Proba-V pixel, all 2x2 m pixels of the
land-cover map without trees are assumed to be stable
throughout the season. An assumption which is confirmed by
the analysis of built-up and grass surface dynamics throughout
the season. The seasonal variation of LAI within the Proba-V
pixel is thus only influenced by the dynamics of the tree
canopies within that pixel. For assessing the leaf area index
(LAI), we use the Sunscan system (Type SS1-COM-R4) to
measure incident and transmitted photosynthetically active
radiation. We measure LAI of all trees within the pixel.
Once the Proba-V pixels are validated, distributed LAI maps
are created.
The land-cover map and seasonal LAI maps are integrated into
a WetSpa-Python model (“WetSpaLAI”) for the simulation of
the water-balance at the surface for one full growing season
(March-November 2015), at a 2 m resolution and for an hourly
time step.
values we calculate based on LAI data. Analyzing the Proba-V
time-series we found that low vegetation (grass and shrubs)
has a low variation in LAI and thus consider a constant
interception storage capacity throughout the season (LAI= 0.6,
Interc = 0.75). The classical WetSpa simulator, however,
varies the interception capacity of low vegetation from 0.5 to 2
mm. This lowers the interception storage capacity in winter
month and increases the capacity in summer months.
1.6
WetSpaClassic
WetSpaLAI
1.2
0.8
III. RESULTS AND CONCLUSIONS
0.4
The seasonal dynamics of a Proba-V pixel are compared to the
measured dynamics of 18 trees within that specific pixel. Fig.
1 illustrates the LAI value of the tree fraction for the given
Proba-V pixel throughout the season compared to the
measured LAI values. Based on Proba-V the LAI values vary
from 2.5 - 4.5 whereas the field measurements result in LAI
values from 0.6 - 4.2 from minimum to maximum conditions
(fig. 1). The difference of LAI for minimum conditions is
because on the Proba-V image as trees lose their leaves LAI is
more and more influenced by the under growing grass. The
field measurements on the other hand are independent of
undergrowth but strongly influenced by the weather conditions
during the day of measurements. The little drops in the curve
(29/07/2015, 09/10/2015) are related to poorer weather
conditions during measurements.
0
January
July
December
2015
Figure 2: Interception storage capacity [mm]
using the classical approach with a sine
function (green) versus the new approach
including LAI maps (yellow)
Additionally, the remote sensing based interception capacity
includes the potential of undergrowth during leafless
conditions which further increases the potential of interception
storage in winter month. We then compare the model results
using the classical WetSpa simulation -at a 100 m resolutionand the new WetSpaLAI that uses the high resolution (2m)
land-cover map and seasonal LAI maps. Fig. 3 illustrates the
surface water balance for the two approaches (AprilSeptember 2015). We can see that the higher resolution model,
including seasonal LAI maps, increases the interception
storage and surface runoff component but decreases
depression storage and infiltration.
Figure 1: LAI dynamics of selected Proba-V pixel at
the VUB campus, Brussels, Belgium.
We introduce the time-series of LAI maps into the WetSpa
model and evaluate the effect on the interception storage
capacity. Fig. 2 shows that in 2015 the season of urban green
started later and lasted longer than expected with the sine
function. Further, the maximum and minimum interception
storage capacity threshold values used within the classical
WetSpa simulator are higher respectively lower than the
Figure 3: The surface water balance [mm] using high
resolution remote sensing data (yellow) vs. the
original WetSpa approach (green) (April –September
2015).
The main conclusions are that (1) we are able to create a
detailed time-series of LAI maps in heterogeneous urban
areas. (2) This allows us to simulate time and location specific
interception storage capacity which (3) influences the amount
of net rainfall and thus the evapotranspiration, runoff and
infiltration capacity of the studied area.
[2]
[3]
[4]
Acknowledgment
This research is co-funded within the framework of the
UrbanEARS project SR/00/307 (BELSPO STEREOIII), and
the BELAIR SONIA project SR/03/333. I also want to
acknowledge Jeroen Degerickx for his contribution to the field
work and Frederik Priem for preparing the land-cover map.
[5]
[6]
[7]
References
[8]
[1]
Bindhu, V. M., & Narasimhan, B. (2015). Development of a spatiotemporal disaggregation method (DisNDVI) for generating a time series
of fine resolution NDVI images. ISPRS Journal of Photogrammetry and
Remote Sensing, 101, 57–68. doi:10.1016/j.isprsjprs.2014.12.005
Konrad, C. P., Booth, D. B., & Washington. (2002). Hydrologic trends
associated with urban development for selected streams in the Puget
Sound Basin, Western Washington. Water-Resources Investigations
Report ;02-4040, (vi, 40 p.), vi, 40 p. doi:10.1007/s10661-010-1363-1
Liu, Y.B., De Smedt, F. (2004). WetSpa extension, a GIS-based
hydrologic model for flood prediction and watershed management.
Department of Hydrology and Hydraulic Engineering
Pataki, D. E., Boone, C. G., Hogue, T. S., Jenerette, G. D., Fadden, J. P.
M., & Pincetl, S. (2011). Ecohydrology Bearings-Invited Commentary
Socio-ecohydrology
and
the
urban
water
challenge.
ECOHYDROLOGY, 4, 341–347.
Priem, F., Canters, F., & Brussel, V. U. (2016). Synergistic use of
LiDAR and APEX hyperspectral data for high-resolution urban land
cover mapping, 1–27.
Wang, Z.M., Batelaan, O. & De Smedt, F., (1997). A distributed model
for water and energy transfer between soil, plants and atmosphere
(WetSpa), Phys. Chem. Earth, 21(3), 189-193.
Su, Z. (1996). Remote sensing applied to hydrology: The Sauer river
basin study. Bochum, Ph.D. Thesis Faculty of Civil Engineering, Ruhr
University Bochum, Germany.
Tucker, C. J. (1979). Red and photographic infrared linear combinations
for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–
150.
doi:10.1016/0034-4257(79)90013-0